The Art of Staying Engaged: The Role of Personal Resources in the Mental Well-Being of Young Veterinary Professionals
Bibliographic record
Abstract
Health care professionals perceive transitions (e.g., from university to professional practice) to be challenging and stressful. The aim of the present research was to identify person-related characteristics that, in addition to work-related aspects, affect the mental well-being and performance of recently graduated veterinary professionals, and to reach a greater understanding of the role of personal resources in mental health and well-being. Based on the Job Demands-Resources (JD-R) model, a questionnaire measuring work engagement as well as burnout and its potential predictors was developed and distributed to 1,760 veterinarians who graduated in the Netherlands between 1999 and 2009 (response rate 41%, of which 73% were females). An intervention aiming at increasing personal resources was evaluated using qualitative and quantitative methods. The intervention was designed so that participants could set their own learning objectives toward which they could work during a yearlong multimodular program. The results show that gender and the number of years after graduation have a small effect on exhaustion resulting in 16% of the veterinarians (18% for females) meeting the criteria for burnout in the first 5 years after graduation. Thirteen percent of respondents could be classified as being highly engaged. While burnout resulted mostly from job characteristics (demands and resources), work engagement resulted mostly from job resources and personal resources. Personal resources appear to have an important mediating and initiating role in work engagement and performance. Self-reported ratings of reflective behavior, proactive behavior, and self-efficacy were significantly increased after a yearlong resources development program. Practical implications are discussed.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".